The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Power plants, wastewater treatment plants, factories, airplanes, and automobiles are some examples of complex systems that include multiple machines operating to accomplish objectives. Understanding and identifying operating conditions of complex systems from data streams produced by those systems allow operators of those systems to monitor and ensure efficient operation of those systems. The ability to identify certain operating conditions allows operators to adjust those systems to avoid unnecessary failure. Identifying impending failure or other conditions typically is done by studying the output values from sensors of various types that are mounted on the machines or systems and produce displays, indicators, or output data streams.
One such technique for monitoring data streams that are produced by complex systems is condition recognition based upon machine learning techniques executed using computers. Implementing machine learning based on condition recognition generally requires a large data set of input values from the data stream and a pre-existing well-formed training data set from which a condition model may be constructed. Given the complexity of typical industrial systems, machine learning algorithms cannot produce good results unless they receive a training data set that is sufficiently large and well correlated with particular conditions. However, even a well-formed training data set that defines the conditions may not consistently predict conditions of the data stream if the environment of the complex system changes or if parts of the complex system change or wear out over time.
Continually evolving conditions and the inability to account for all conditions within a well-formed training data set make implementing machine learning techniques for condition recognition difficult.